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About This Role
Job ID: 1161457\_RR00119626
Facility: NYU Grossman School of Medicine
Position Type: Full\-Time/Regular
Shift: Day
Schedule: 9am\-5pm
Department: IT/Health IT/Informatics, MCIT\-Research\-DataCore (S1724\), NYU Grossman School of Medicine,
NYU Grossman School of Medicine is one of the nation’s top\-ranked medical schools. For 175 years, NYU Grossman School of Medicine has trained thousands of physicians and scientists who have helped to shape the course of medical history and enrich the lives of countless people. An integral part of NYU Langone Health, the Grossman School of Medicine at its core is committed to improving the human condition through medical education, scientific research, and direct patient care. At NYU Langone Health, equity and inclusion are fundamental values. We strive to be a place where our exceptionally talented faculty, staff, and students of all identities can thrive. We embrace inclusion and individual skills, ideas, and knowledge.
For more information, go to med.nyu.edu, and interact with us on LinkedIn, Glassdoor, Indeed, Facebook, Twitter and Instagram.
Position Summary:
We have an exciting opportunity to join our team as a Sr. Engineer I, AI.
NYU Langone Health seeks an experienced Data Science \& AI Engineer to design, build, deploy, and govern enterprise\-grade AI and machine learning capabilities that support clinical care, research, operational analytics, and learning health system initiatives across the institution. This role is situated within the Medical Center Information Technology (MCIT) and is focused on establishing durable institutional infrastructure rather than serving a single project or point solution. The position is intended for a technically strong engineer who can translate clinical and operational priorities into scalable AI systems, production\-ready data products, and reusable platform capabilities aligned with enterprise standards and governance expectations.
This position combines advanced software engineering, applied machine learning, health data architecture, and technical leadership. The successful candidate will bring hands\-on experience deploying models and AI services in production environments, with strong command of cloud infrastructure, CI/CD, orchestration, containerization, data lake and lakehouse patterns, and modern MLOps practices. Particular emphasis is placed on practical use of AI\-assisted engineering tools such as GitHub Copilot, OpenAI Codex, Claude Code, and related platforms to accelerate high\-quality development, improve documentation, and support disciplined code review and engineering productivity.
The ideal candidate will have demonstrated expertise in healthcare data environments including clinical data warehouses, OMOP Common Data Model, EHR integration, biomedical ontologies, semantic data models, knowledge graphs, and real\-world evidence pipelines. This role also requires strong capability in NLP and LLM\-based systems for clinical text understanding, phenotyping, knowledge extraction, and decision support, alongside deep familiarity with agentic AI patterns, trustworthy AI, model monitoring, fairness evaluation, explainability, and regulatory expectations for AI deployment in healthcare settings.
Job Responsibilities:
AI Engineering and Platform Delivery
Build, deploy, and support production AI/ML, NLP, LLM, and agentic systems for clinical, research, and operational use cases.
Design scalable model deployment patterns, inference services, feature pipelines, and reusable AI components for enterprise use.
Implement MLOps standards for model lifecycle management, registry governance, monitoring, retraining, versioning, and release control.
Develop agentic workflows that combine LLM reasoning, retrieval, tool use, orchestration, memory, and human oversight within defined safety boundaries.
Health Data and Clinical Integration
Engineer data pipelines across clinical data warehouses, OMOP\-CDM, data lakes, and lakehouse platforms to support AI, analytics, and real\-world evidence workflows.
Build multimodal pipelines that integrate structured EHR data, clinical text, imaging metadata, genomics, and other clinical data sources.
Deploy predictive models integrated with Epic and related EHR systems for clinical decision support, patient screening, clinical trial matching, cohort identification, and point\-of\-care risk prediction.
Partner with Clinical Informatics and engineering teams to integrate AI services into operational workflows with appropriate usability, reliability, and traceability.
Trustworthy AI and Technical Leadership
Implement controls for drift detection, fairness assessment, explainability, hallucination detection, validation, and regulatory alignment in deployed AI systems.
Establish coding, review, testing, documentation, and deployment standards for AI engineering across teams.
Mentor developers and lead architecture decisions for reusable pipelines, APIs, services, and deployment patterns.
Contribute to institutional AI governance, technical training, and enterprise enablement of responsible AI adoption.
Minimum Qualifications:
To qualify you must have a 5\+ years of hands\-on experience designing, deploying, and supporting AI/ML systems in production environments, in healthcare, life sciences, or other regulated settings.
Demonstrated experience with enterprise MLOps practices including model registry management, CI/CD for ML, monitoring, observability, retraining workflows, and operational governance.
Proven experience building and deploying NLP, LLM, agentic AI, or other applied AI systems for clinical text understanding, knowledge extraction, phenotyping, clinical workflow support, or real\-world evidence applications.
Experience with healthcare data platforms including clinical data warehouses, OMOP\-CDM or similar common data models, and EHR\-derived data assets.
Strong experience with cloud services and platform engineering on Azure and AWS, including secure data services, compute orchestration, storage patterns, and deployment automation.
Experience implementing scalable data and ML pipelines using Databricks, Apache Spark, and data lake or lakehouse architectures.
Experience with containerized application and model deployment using Docker, Kubernetes, and workflow orchestration frameworks.
Experience deploying predictive models, CDS\-oriented services, trial matching solutions, or patient screening workflows integrated with Epic or similar EHR platforms is strongly preferred.
Demonstrated leadership mentoring technical staff, conducting code and model reviews, defining engineering standards, and guiding architecture decisions across multiple stakeholders.
Expert proficiency in Python for data engineering, machine learning engineering, API development, automation, and platform integration.
Strong proficiency in SQL and experience with relational, analytical, and NoSQL data platforms.
Extensive hands\-on experience with AI\-assisted development tools such as GitHub Copilot, OpenAI Codex, Claude Code, or similar coding copilots for engineering acceleration and quality improvement.
Strong command of MLOps and ML platform technologies such as MLflow, Kubeflow, Azure Machine Learning, AWS SageMaker, Databricks ML, or equivalent frameworks.
Experience with CI/CD platforms and engineering workflows including GitHub Actions, Azure DevOps, GitLab CI/CD, Jenkins, or comparable systems.
Proficiency with orchestration and pipeline frameworks such as Apache Airflow, Prefect, Dagster, Argo Workflows, or similar tools.
Solid understanding of containerization and scalable deployment frameworks including Docker, Kubernetes, Helm, and infrastructure\-as\-code approaches.
Experience with Apache Spark, distributed data processing, and large\-scale data engineering patterns for model training and feature generation.
Experience with Databricks for collaborative analytics, scalable ETL, feature engineering, ML experimentation, and production deployment workflows.
Proficiency with machine learning and deep learning frameworks such as scikit\-learn, XGBoost, LightGBM, PyTorch, TensorFlow, or JAX.
Experience with NLP, LLM, and agentic AI toolchains such as Hugging Face Transformers, spaCy, LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, vector databases, prompt and evaluation frameworks, and retrieval\-augmented generation patterns.
Knowledge of healthcare data standards and models including OMOP Common Data Model, HL7 FHIR, SNOMED CT, LOINC, RxNorm, and related semantic assets.
Experience with version control, collaborative software development, testing, and release management practices in team environments.
Preferred Qualifications:
Experience integrating with Epic, FHIR APIs, HL7 interfaces, SMART on FHIR applications, Epic CDS workflows, BestPractice Advisories, In Basket, or related clinical systems.
Familiarity with feature stores, online/offline feature serving patterns, and real\-time inference infrastructure.
Experience with multimodal or foundation models spanning text, imaging, genomics, and structured clinical data.
Experience with GPU orchestration, distributed training frameworks, and model optimization for scale and cost control.
Knowledge of causal inference, reinforcement learning, human\-in\-the\-loop systems, and adaptive trial or learning health system methodologies.
Experience with semantic web technologies, RDF/SPARQL, graph databases, Neo4j, or biomedical knowledge graph implementation.
Epic integration patterns, FHIR APIs, HL7 interfaces, SMART on FHIR, or Epic CDS workflows.
Multimodal modeling across text, imaging, genomics, and structured clinical data.
Knowledge graphs, semantic web technologies, and biomedical ontology implementation.
Causal inference, reinforcement learning, or human\-in\-the\-loop decision systems.
AI Governance and Professional Competencies
Strong knowledge of fairness, subgroup evaluation, explainability, drift monitoring, and validation in healthcare AI.
Understanding of HIPAA, privacy, security, and applicable FDA considerations for clinical AI deployment.
Ability to translate clinical and operational requirements into scalable technical designs.
Strong communication, documentation, and cross\-functional collaboration skills.
Commitment to code quality, testing, observability, and operational reliability.
Qualified candidates must be able to effectively communicate with all levels of the organization.
NYU Grossman School of Medicine provides its staff with far more than just a place to work. Rather, we are an institution you can be proud of, an institution where you’ll feel good about devoting your time and your talents. At NYU Langone Health, we are committed to supporting our workforce and their loved ones with a comprehensive benefits and wellness package. Our offerings provide a robust support system for any stage of life, whether it’s developing your career, starting a family, or saving for retirement. The support employees receive goes beyond a standard benefit offering, where employees have access to financial security benefits, a generous time\-off program and employee resources groups for peer support. Additionally, all employees have access to our holistic employee wellness program, which focuses on seven key areas of well\-being: physical, mental, nutritional, sleep, social, financial, and preventive care. The benefits and wellness package is designed to allow you to focus on what truly matters. Join us and experience the extensive resources and services designed to enhance your overall quality of life for you and your family.
NYU Grossman School of Medicine is an equal opportunity employer and committed to inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration. We require applications to be completed online.
View Know Your Rights: Workplace discrimination is illegal.
NYU Langone Health provides a salary range to comply with the New York state Law on Salary Transparency in Job Advertisements. The salary range for the role is $101,493\.51 – $147,000\.00 Annually. Actual salaries depend on a variety of factors, including experience, specialty, education, and hospital need. The salary range or contractual rate listed does not include bonuses/incentive, differential pay or other forms of compensation or benefits.
To view the Pay Transparency Notice, please click here
Salaries shown on independent jobs related websites reflect market averages and do not represent information obtained directly from NYU Langone. We invite and encourage each candidate to discuss salary/hourly specifics during the application and hiring process.
NYU Langone Health seeks an experienced Data Science \& AI Engineer to design, build, deploy, and govern enterprise\-grade AI and machine learning capabilities that support clinical care, research, operational analytics, and learning health system initiatives across the institution. This role is situated within the Medical Center Information Technology (MCIT) and is focused on establishing durable institutional infrastructure rather than serving a single project or point solution. The position is intended for a technically strong engineer who can translate clinical and operational priorities into scalable AI systems, production\-ready data products, and reusable platform capabilities aligned with enterprise standards and governance expectations.
This position combines advanced software engineering, applied machine learning, health data architecture, and technical leadership. The successful candidate will bring hands\-on experience deploying models and AI services in production environments, with strong command of cloud infrastructure, CI/CD, orchestration, containerization, data lake and lakehouse patterns, and modern MLOps practices. Particular emphasis is placed on practical use of AI\-assisted engineering tools such as GitHub Copilot, OpenAI Codex, Claude Code, and related platforms to accelerate high\-quality development, improve documentation, and support disciplined code review and engineering productivity.
The ideal candidate will have demonstrated expertise in healthcare data environments including clinical data warehouses, OMOP Common Data Model, EHR integration, biomedical ontologies, semantic data models, knowledge graphs, and real\-world evidence pipelines. This role also requires strong capability in NLP and LLM\-based systems for clinical text understanding, phenotyping, knowledge extraction, and decision support, alongside deep familiarity with agentic AI patterns, trustworthy AI, model monitoring, fairness evaluation, explainability, and regulatory expectations for AI deployment in healthcare settings.
Job Responsibilities:
AI Engineering and Platform Delivery
Build, deploy, and support production AI/ML, NLP, LLM, and agentic systems for clinical, research, and operational use cases.
Design scalable model deployment patterns, inference services, feature pipelines, and reusable AI components for enterprise use.
Implement MLOps standards for model lifecycle management, registry governance, monitoring, retraining, versioning, and release control.
Develop agentic workflows that combine LLM reasoning, retrieval, tool use, orchestration, memory, and human oversight within defined safety boundaries.
Health Data and Clinical Integration
Engineer data pipelines across clinical data warehouses, OMOP\-CDM, data lakes, and lakehouse platforms to support AI, analytics, and real\-world evidence workflows.
Build multimodal pipelines that integrate structured EHR data, clinical text, imaging metadata, genomics, and other clinical data sources.
Deploy predictive models integrated with Epic and related EHR systems for clinical decision support, patient screening, clinical trial matching, cohort identification, and point\-of\-care risk prediction.
Partner with Clinical Informatics and engineering teams to integrate AI services into operational workflows with appropriate usability, reliability, and traceability.
Trustworthy AI and Technical Leadership
Implement controls for drift detection, fairness assessment, explainability, hallucination detection, validation, and regulatory alignment in deployed AI systems.
Establish coding, review, testing, documentation, and deployment standards for AI engineering across teams.
Mentor developers and lead architecture decisions for reusable pipelines, APIs, services, and deployment patterns.
Contribute to institutional AI governance, technical training, and enterprise enablement of responsible AI adoption.
Minimum Qualifications:
To qualify you must have a 5\+ years of hands\-on experience designing, deploying, and supporting AI/ML systems in production environments, in healthcare, life sciences, or other regulated settings.
Demonstrated experience with enterprise MLOps practices including model registry management, CI/CD for ML, monitoring, observability, retraining workflows, and operational governance.
Proven experience building and deploying NLP, LLM, agentic AI, or other applied AI systems for clinical text understanding, knowledge extraction, phenotyping, clinical workflow support, or real\-world evidence applications.
Experience with healthcare data platforms including clinical data warehouses, OMOP\-CDM or similar common data models, and EHR\-derived data assets.
Strong experience with cloud services and platform engineering on Azure and AWS, including secure data services, compute orchestration, storage patterns, and deployment automation.
Experience implementing scalable data and ML pipelines using Databricks, Apache Spark, and data lake or lakehouse architectures.
Experience with containerized application and model deployment using Docker, Kubernetes, and workflow orchestration frameworks.
Experience deploying predictive models, CDS\-oriented services, trial matching solutions, or patient screening workflows integrated with Epic or similar EHR platforms is strongly preferred.
Demonstrated leadership mentoring technical staff, conducting code and model reviews, defining engineering standards, and guiding architecture decisions across multiple stakeholders.
Expert proficiency in Python for data engineering, machine learning engineering, API development, automation, and platform integration.
Strong proficiency in SQL and experience with relational, analytical, and NoSQL data platforms.
Extensive hands\-on experience with AI\-assisted development tools such as GitHub Copilot, OpenAI Codex, Claude Code, or similar coding copilots for engineering acceleration and quality improvement.
Strong command of MLOps and ML platform technologies such as MLflow, Kubeflow, Azure Machine Learning, AWS SageMaker, Databricks ML, or equivalent frameworks.
Experience with CI/CD platforms and engineering workflows including GitHub Actions, Azure DevOps, GitLab CI/CD, Jenkins, or comparable systems.
Proficiency with orchestration and pipeline frameworks such as Apache Airflow, Prefect, Dagster, Argo Workflows, or similar tools.
Solid understanding of containerization and scalable deployment frameworks including Docker, Kubernetes, Helm, and infrastructure\-as\-code approaches.
Experience with Apache Spark, distributed data processing, and large\-scale data engineering patterns for model training and feature generation.
Experience with Databricks for collaborative analytics, scalable ETL, feature engineering, ML experimentation, and production deployment workflows.
Proficiency with machine learning and deep learning frameworks such as scikit\-learn, XGBoost, LightGBM, PyTorch, TensorFlow, or JAX.
Experience with NLP, LLM, and agentic AI toolchains such as Hugging Face Transformers, spaCy, LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, vector databases, prompt and evaluation frameworks, and retrieval\-augmented generation patterns.
Knowledge of healthcare data standards and models including OMOP Common Data Model, HL7 FHIR, SNOMED CT, LOINC, RxNorm, and related semantic assets.
Experience with version control, collaborative software development, testing, and release management practices in team environments.
Preferred Qualifications:
Experience integrating with Epic, FHIR APIs, HL7 interfaces, SMART on FHIR applications, Epic CDS workflows, BestPractice Advisories, In Basket, or related clinical systems.
Familiarity with feature stores, online/offline feature serving patterns, and real\-time inference infrastructure.
Experience with multimodal or foundation models spanning text, imaging, genomics, and structured clinical data.
Experience with GPU orchestration, distributed training frameworks, and model optimization for scale and cost control.
Knowledge of causal inference, reinforcement learning, human\-in\-the\-loop systems, and adaptive trial or learning health system methodologies.
Experience with semantic web technologies, RDF/SPARQL, graph databases, Neo4j, or biomedical knowledge graph implementation.
Epic integration patterns, FHIR APIs, HL7 interfaces, SMART on FHIR, or Epic CDS workflows.
Multimodal modeling across text, imaging, genomics, and structured clinical data.
Knowledge graphs, semantic web technologies, and biomedical ontology implementation.
Causal inference, reinforcement learning, or human\-in\-the\-loop decision systems.
AI Governance and Professional Competencies
Strong knowledge of fairness, subgroup evaluation, explainability, drift monitoring, and validation in healthcare AI.
Understanding of HIPAA, privacy, security, and applicable FDA considerations for clinical AI deployment.
Ability to translate clinical and operational requirements into scalable technical designs.
Strong communication, documentation, and cross\-functional collaboration skills.
Commitment to code quality, testing, observability, and operational reliability.
Qualified candidates must be able to effectively communicate with all levels of the organization.
NYU Grossman School of Medicine provides its staff with far more than just a place to work. Rather, we are an institution you can be proud of, an institution where you’ll feel good about devoting your time and your talents. At NYU Langone Health, we are committed to supporting our workforce and their loved ones with a comprehensive benefits and wellness package. Our offerings provide a robust support system for any stage of life, whether it’s developing your career, starting a family, or saving for retirement. The support employees receive goes beyond a standard benefit offering, where employees have access to financial security benefits, a generous time\-off program and employee resources groups for peer support. Additionally, all employees have access to our holistic employee wellness program, which focuses on seven key areas of well\-being: physical, mental, nutritional, sleep, social, financial, and preventive care. The benefits and wellness package is designed to allow you to focus on what truly matters. Join us and experience the extensive resources and services designed to enhance your overall quality of life for you and your family.
NYU Grossman School of Medicine is an equal opportunity employer and committed to inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration. We require applications to be completed online.
View Know Your Rights: Workplace discrimination is illegal.
NYU Langone Health provides a salary range to comply with the New York state Law on Salary Transparency in Job Advertisements. The salary range for the role is $101,493\.51 – $147,000\.00 Annually. Actual salaries depend on a variety of factors, including experience, specialty, education, and hospital need. The salary range or contractual rate listed does not include bonuses/incentive, differential pay or other forms of compensation or benefits.
To view the Pay Transparency Notice, please click here
Salaries shown on independent jobs related websites reflect market averages and do not represent information obtained directly from NYU Langone. We invite and encourage each candidate to discuss salary/hourly specifics during the application and hiring process.
About NYU Langone Health
Be Where Everyone Is Dedicated to Exceptional Care
NYU Langone is a world\-class, patient\-centered, integrated academic health system with Magnet®\-recognized status by the American Nurses Credentialing Center (ANCC). Our trifold mission to care, teach, and discover is achieved daily through NYU Langone’s inclusive culture devoted to excellence across the organization. Here, you can advance your career supported by exceptionally talented faculty and staff in an environment where everyone works together to deliver the best possible outcomes for our patients.
Our Hiring Process
Joining Our Team
Get ready to start your career journey at NYU Langone, where cutting\-edge research meets compassionate care, and discover how you can contribute to shaping the future of medicine.
Step 1
### Apply Online
The NYU Langone hiring process begins with you applying through our online portal. Be sure to update and upload your resume. Shortly after you submit your application, you will receive an email confirmation. Ten days after applying you will receive a talent assessment to be completed.
Step 2
### Schedule Interviews
If selected to continue the interview process, HR will reach out via phone or email first. Then, depending on your position, they will schedule an interview with unit managers or team members. You are encouraged to dress professionally for all interviews.
Step 3
### Receive Offer
If you successfully complete the interview process and are identified as a finalist for the position, we will require that you complete a professional reference process. After evaluating the completed references, a decision will be made on who will receive a preliminary offer. If you receive a preliminary offer, HR will start the onboarding process with an agreed\-upon tentative start date.
Step 4
### Training \& Orientation
You will be contacted by an onboarding specialist who will work with you on your pre\-boarding requirements. Once fully cleared, we will ask you to complete compliance orientation regulatory training. On your first day, you will attend an online required orientation to acclimate to the health system and report to your new department based on instructions provided by your hiring manager.
Salary Context
This $101K-$147K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At NYU Langone Health, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($124K) sits 33% below the category median. Disclosed range: $101K to $147K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
NYU Langone Health AI Hiring
NYU Langone Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $147K - $147K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 tracked positions. That's 5% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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